CoReg.py 77.8 KB
Newer Older
1
2
3
4
5
6
7
8
9
# -*- coding: utf-8 -*-
__author__='Daniel Scheffler'

import os
import re
import shutil
import subprocess
import time
import warnings
10
from copy import copy
11
12

# custom
13
14
15
16
try:
    import gdal
except ImportError:
    from osgeo import gdal
17
import numpy as np
18
19
20
try:
    import pyfftw
except ImportError:
21
    pyfftw = None
22
from shapely.geometry import Point, Polygon
23
from skimage.exposure import rescale_intensity
24
25

# internal modules
26
from .DeShifter import DESHIFTER, _dict_rspAlg_rsp_Int
27
28
29
30
31
32
from .          import geometry  as GEO
from .          import io        as IO
from .          import plotting  as PLT

from py_tools_ds.ptds                      import GeoArray
from py_tools_ds.ptds.geo.coord_calc       import corner_coord_to_minmax, get_corner_coordinates
33
from py_tools_ds.ptds.geo.vector.topology  import get_overlap_polygon, get_smallest_boxImYX_that_contains_boxMapYX
34
from py_tools_ds.ptds.geo.projection       import prj_equal, get_proj4info
35
36
from py_tools_ds.ptds.geo.vector.geometry  import boxObj, round_shapelyPoly_coords
from py_tools_ds.ptds.geo.coord_grid       import move_shapelyPoly_to_image_grid
37
from py_tools_ds.ptds.geo.coord_trafo      import pixelToMapYX, reproject_shapelyGeometry, mapXY2imXY
38
39
40
from py_tools_ds.ptds.geo.raster.reproject import warp_ndarray
from py_tools_ds.ptds.geo.map_info         import geotransform2mapinfo
from py_tools_ds.ptds.numeric.vector       import find_nearest
41
from py_tools_ds.ptds.similarity.raster    import calc_ssim
42
43
44
45




46
class GeoArray_CoReg(GeoArray):
47
48
    def __init__(self, CoReg_params, imID):
        assert imID in ['ref', 'shift']
Daniel Scheffler's avatar
CoReg:    
Daniel Scheffler committed
49

50
51
52
53
54
55
        # run GeoArray init
        path_or_geoArr = CoReg_params['im_ref'] if imID == 'ref' else CoReg_params['im_tgt']
        nodata         = CoReg_params['nodata'][0 if imID == 'ref' else 1]
        progress       = CoReg_params['progress']
        q              = CoReg_params['q'] if not CoReg_params['v'] else False

56
        super(GeoArray_CoReg, self).__init__(path_or_geoArr, nodata=nodata, progress=progress, q=q)
57
58

        self.imID   = imID
59
        self.imName = 'reference image' if imID == 'ref' else 'image to be shifted'
60
61
62
        self.v      = CoReg_params['v']

        assert isinstance(self, GeoArray), \
63
64
65
66
            'Something went wrong with the creation of GeoArray instance for the %s. The created ' \
            'instance does not seem to belong to the GeoArray class. If you are working in Jupyter Notebook, reset the ' \
            'kernel and try again.' %self.imName

67
        # set title to be used in plots
68
        self.title = os.path.basename(self.filePath) if self.filePath else self.imName
69
70
71
72
73
74
75

        # validate params
        assert self.prj, 'The %s has no projection.' % self.imName
        assert not re.search('LOCAL_CS', self.prj), 'The %s is not georeferenced.' % self.imName
        assert self.gt, 'The %s has no map information.' % self.imName

        # set band4match
76
        self.band4match = (CoReg_params['r_b4match'] if imID == 'ref' else CoReg_params['s_b4match'])-1
77
78
79
        assert self.bands >= self.band4match+1 >= 1, "The %s has %s %s. So its band number to match must be %s%s. " \
            "Got %s." % (self.imName, self.bands, 'bands' if self.bands > 1 else 'band', 'between 1 and '
            if self.bands > 1 else '', self.bands, self.band4match)
80

81
82
83
84
85
        # set footprint_poly
        given_footprint_poly = CoReg_params['footprint_poly_%s' % ('ref' if imID == 'ref' else 'tgt')]
        given_corner_coord   = CoReg_params['data_corners_%s'   % ('ref' if imID == 'ref' else 'tgt')]

        if given_footprint_poly:
86
            self.footprint_poly = given_footprint_poly
87
        elif given_corner_coord is not None:
88
            self.footprint_poly = Polygon(given_corner_coord)
89
90
        elif not CoReg_params['calc_corners']:
            # use the image extent
91
            self.footprint_poly = Polygon(get_corner_coordinates(gt=self.gt, cols=self.cols,rows=self.rows))
92
        else:
93
94
95
            # footprint_poly is calculated automatically by GeoArray
            if not CoReg_params['q']:
                print('Calculating actual data corner coordinates for %s...' % self.imName)
96
            self.calc_mask_nodata(fromBand=self.band4match)  # this avoids that all bands have to be read
97

98
        self.poly = self.footprint_poly  # returns a shapely geometry
99

100
        if not self.q:
101
            print('Bounding box of calculated footprint for %s:\n\t%s' % (self.imName, self.poly.bounds))
102

103
104
105
        # add bad data mask
        given_mask = CoReg_params['mask_baddata_%s' % ('ref' if imID == 'ref' else 'tgt')]
        if given_mask:
106
            self.mask_baddata = given_mask
107

108
109
110


class COREG(object):
111
112
    """See help(COREG) for documentation!"""

113
114
    def __init__(self, im_ref, im_tgt, path_out=None, fmt_out='ENVI', out_crea_options=None, r_b4match=1, s_b4match=1,
                 wp=(None,None), ws=(512, 512), max_iter=5, max_shift=5, align_grids=False, match_gsd=False,
115
116
                 out_gsd=None, target_xyGrid=None, resamp_alg_deshift='cubic', resamp_alg_calc='cubic',
                 footprint_poly_ref=None, footprint_poly_tgt=None, data_corners_ref=None, data_corners_tgt=None,
117
118
119
                 nodata=(None,None), calc_corners=True, binary_ws=True, mask_baddata_ref=None, mask_baddata_tgt=None,
                 multiproc=True, force_quadratic_win=True, progress=True, v=False, path_verbose_out=None, q=False,
                 ignore_errors=False):
120
121
122
123

        """Detects and corrects global X/Y shifts between a target and refernce image. Geometric shifts are calculated
        at a specific (adjustable) image position. Correction performs a global shifting in X- or Y direction.

124
125
126
127
        :param im_ref(str, GeoArray):   source path (any GDAL compatible image format is supported) or GeoArray instance
                                        of reference image
        :param im_tgt(str, GeoArray):   source path (any GDAL compatible image format is supported) or GeoArray instance
                                        of image to be shifted
128
        :param path_out(str):           target path of the coregistered image
129
130
131
                                            - if None (default), the method correct_shifts() does not write to disk
                                            - if 'auto': /dir/of/im1/<im1>__shifted_to__<im0>.bsq
        :param fmt_out(str):            raster file format for output file. ignored if path_out is None. can be any GDAL
132
133
                                        compatible raster file format (e.g. 'ENVI', 'GeoTIFF'; default: ENVI). Refer to
                                        http://www.gdal.org/formats_list.html to get a full list of supported formats.
134
135
        :param out_crea_options(list):  GDAL creation options for the output image,
                                        e.g. ["QUALITY=80", "REVERSIBLE=YES", "WRITE_METADATA=YES"]
136
137
138
139
140
141
142
143
144
145
146
        :param r_b4match(int):          band of reference image to be used for matching (starts with 1; default: 1)
        :param s_b4match(int):          band of shift image to be used for matching (starts with 1; default: 1)
        :param wp(tuple):               custom matching window position as map values in the same projection like the
                                        reference image (default: central position of image overlap)
        :param ws(tuple):               custom matching window size [pixels] (default: (512,512))
        :param max_iter(int):           maximum number of iterations for matching (default: 5)
        :param max_shift(int):          maximum shift distance in reference image pixel units (default: 5 px)
        :param align_grids(bool):       align the coordinate grids of the image to be and the reference image (default: 0)
        :param match_gsd(bool):         match the output pixel size to pixel size of the reference image (default: 0)
        :param out_gsd(tuple):          xgsd ygsd: set the output pixel size in map units
                                        (default: original pixel size of the image to be shifted)
147
148
        :param target_xyGrid(list):     a list with a target x-grid and a target y-grid like [[15,45], [15,45]]
                                        This overrides 'out_gsd', 'align_grids' and 'match_gsd'.
149
150
151
152
153
154
155
156
157
        :param resamp_alg_deshift(str)  the resampling algorithm to be used for shift correction (if neccessary)
                                        valid algorithms: nearest, bilinear, cubic, cubic_spline, lanczos, average, mode,
                                                          max, min, med, q1, q3
                                        default: cubic
        :param resamp_alg_calc(str)     the resampling algorithm to be used for all warping processes during calculation
                                        of spatial shifts
                                        (valid algorithms: nearest, bilinear, cubic, cubic_spline, lanczos, average, mode,
                                                       max, min, med, q1, q3)
                                        default: cubic (highly recommended)
158
159
160
161
162
163
164
165
166
167
        :param footprint_poly_ref(str): footprint polygon of the reference image (WKT string or shapely.geometry.Polygon),
                                        e.g. 'POLYGON ((299999 6000000, 299999 5890200, 409799 5890200, 409799 6000000,
                                                        299999 6000000))'
        :param footprint_poly_tgt(str): footprint polygon of the image to be shifted (WKT string or shapely.geometry.Polygon)
                                        e.g. 'POLYGON ((299999 6000000, 299999 5890200, 409799 5890200, 409799 6000000,
                                                        299999 6000000))'
        :param data_corners_ref(list):  map coordinates of data corners within reference image.
                                        ignored if footprint_poly_ref is given.
        :param data_corners_tgt(list):  map coordinates of data corners within image to be shifted.
                                        ignored if footprint_poly_tgt is given.
168
169
170
171
172
        :param nodata(tuple):           no data values for reference image and image to be shifted
        :param calc_corners(bool):      calculate true positions of the dataset corners in order to get a useful
                                        matching window position within the actual image overlap
                                        (default: 1; deactivated if '-cor0' and '-cor1' are given
        :param binary_ws(bool):         use binary X/Y dimensions for the matching window (default: 1)
173
174
175
176
177
178
179
180
181
182
183
184
185
        :param mask_baddata_ref(str, GeoArray): path to a 2D boolean mask file (or an instance of GeoArray) for the
                                                reference image where all bad data pixels (e.g. clouds) are marked with
                                                True and the remaining pixels with False. Must have the same geographic
                                                extent and projection like 'im_ref'. The mask is used to check if the
                                                chosen matching window position is valid in the sense of useful data.
                                                Otherwise this window position is rejected.
        :param mask_baddata_tgt(str, GeoArray): path to a 2D boolean mask file (or an instance of GeoArray) for the
                                                image to be shifted where all bad data pixels (e.g. clouds) are marked
                                                with True and the remaining pixels with False. Must have the same
                                                geographic extent and projection like 'im_ref'. The mask is used to
                                                check if the chosen matching window position is valid in the sense of
                                                useful data. Otherwise this window position is rejected.
        :param multiproc(bool):         enable multiprocessing (default: 1)
186
        :param force_quadratic_win(bool):   force a quadratic matching window (default: 1)
187
        :param progress(bool):          show progress bars (default: True)
188
        :param v(bool):                 verbose mode (default: False)
189
190
        :param path_verbose_out(str):   an optional output directory for intermediate results
                                        (if not given, no intermediate results are written to disk)
191
192
        :param q(bool):                 quiet mode (default: False)
        :param ignore_errors(bool):     Useful for batch processing. (default: False)
193
194
195
196
197
198
                                        In case of error COREG.success == False and COREG.x_shift_px/COREG.y_shift_px
                                        is None
        """

        self.params              = dict([x for x in locals().items() if x[0] != "self"])

199
        # assertions
200
        assert gdal.GetDriverByName(fmt_out), "'%s' is not a supported GDAL driver." % fmt_out
201
202
        if match_gsd and out_gsd: warnings.warn("'-out_gsd' is ignored because '-match_gsd' is set.\n")
        if out_gsd:  assert isinstance(out_gsd, list) and len(out_gsd) == 2, 'out_gsd must be a list with two values.'
203
204
205
206
        if data_corners_ref and not isinstance(data_corners_ref[0], list): # group if not [[x,y],[x,y]..] but [x,y,x,y,]
            data_corners_ref = [data_corners_ref[i:i + 2] for i in range(0, len(data_corners_ref), 2)]
        if data_corners_tgt and not isinstance(data_corners_tgt[0], list): # group if not [[x,y],[x,y]..]
            data_corners_tgt = [data_corners_tgt[i:i + 2] for i in range(0, len(data_corners_tgt), 2)]
207
208
        if nodata: assert isinstance(nodata, tuple) and len(nodata) == 2, "'nodata' must be a tuple with two values." \
                                                                          "Got %s with length %s." %(type(nodata),len(nodata))
209
        for rspAlg in [resamp_alg_deshift, resamp_alg_calc]:
210
            assert rspAlg in _dict_rspAlg_rsp_Int.keys(), "'%s' is not a supported resampling algorithm." % rspAlg
211
        if resamp_alg_calc=='average' and (v or not q):
212
            warnings.warn("The resampling algorithm 'average' causes sinus-shaped patterns in fft images that will "
213
214
                          "affect the precision of the calculated spatial shifts! It is highly recommended to "
                          "choose another resampling algorithm.")
215
216

        self.path_out            = path_out            # updated by self.set_outpathes
217
        self.fmt_out             = fmt_out
218
        self.out_creaOpt         = out_crea_options
219
220
221
222
223
224
225
        self.win_pos_XY          = wp                  # updated by self.get_opt_winpos_winsize()
        self.win_size_XY         = ws                  # updated by self.get_opt_winpos_winsize()
        self.max_iter            = max_iter
        self.max_shift           = max_shift
        self.align_grids         = align_grids
        self.match_gsd           = match_gsd
        self.out_gsd             = out_gsd
226
        self.target_xyGrid       = target_xyGrid
227
228
        self.rspAlg_DS           = resamp_alg_deshift
        self.rspAlg_calc         = resamp_alg_calc
229
230
231
232
233
234
        self.calc_corners        = calc_corners
        self.mp                  = multiproc
        self.bin_ws              = binary_ws
        self.force_quadratic_win = force_quadratic_win
        self.v                   = v
        self.path_verbose_out    = path_verbose_out
235
236
237
        self.q                   = q if not v else False # overridden by v
        self.progress            = progress if not q else False  # overridden by q

238
239
240
241
        self.ignErr              = ignore_errors
        self.max_win_sz_changes  = 3                   # TODO: änderung der window size, falls nach max_iter kein valider match gefunden
        self.ref                 = None                # set by self.get_image_params
        self.shift               = None                # set by self.get_image_params
242
243
244
245
        self.matchBox            = None                # set by self.get_clip_window_properties()  => boxObj
        self.otherBox            = None                # set by self.get_clip_window_properties()  => boxObj
        self.matchWin            = None                # set by self._get_image_windows_to_match() => GeoArray
        self.otherWin            = None                # set by self._get_image_windows_to_match() => GeoArray
246
        self.imfft_gsd           = None                # set by self.get_clip_window_properties()
247
        self.fftw_works          = None                # set by self._calc_shifted_cross_power_spectrum()
248
        self.fftw_win_size_YX    = None                # set by calc_shifted_cross_power_spectrum()
249
250
251
252
253

        self.x_shift_px          = None                # always in shift image units (image coords) # set by calculate_spatial_shifts()
        self.y_shift_px          = None                # always in shift image units (image coords) # set by calculate_spatial_shifts()
        self.x_shift_map         = None                # set by self.get_updated_map_info()
        self.y_shift_map         = None                # set by self.get_updated_map_info()
254
255
        self.vec_length_map      = None
        self.vec_angle_deg       = None
256
        self.updated_map_info    = None                # set by self.get_updated_map_info()
257
258
259
        self.ssim_orig           = None                # set by self._validate_ssim_improvement()
        self.ssim_deshifted      = None                # set by self._validate_ssim_improvement()
        self._ssim_improved      = None                # private attribute to be filled by self.ssim_improved
260
        self.shift_reliability   = None                # set by self.calculate_spatial_shifts()
261
262

        self.tracked_errors      = []                  # expanded each time an error occurs
263
        self.success             = None                # default
264
        self.deshift_results     = None                # set by self.correct_shifts()
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287

        gdal.AllRegister()
        self._get_image_params()
        self._set_outpathes(im_ref, im_tgt)
        self.grid2use                 = 'ref' if self.shift.xgsd <= self.ref.xgsd else 'shift'
        if self.v: print('resolutions: ', self.ref.xgsd, self.shift.xgsd)

        overlap_tmp                   = get_overlap_polygon(self.ref.poly, self.shift.poly, self.v)
        self.overlap_poly             = overlap_tmp['overlap poly'] # has to be in reference projection
        assert self.overlap_poly, 'The input images have no spatial overlap.'
        self.overlap_percentage       = overlap_tmp['overlap percentage']
        self.overlap_area             = overlap_tmp['overlap area']

        if self.v and self.path_verbose_out:
            IO.write_shp(os.path.join(self.path_verbose_out, 'poly_imref.shp'),    self.ref.poly,     self.ref.prj)
            IO.write_shp(os.path.join(self.path_verbose_out, 'poly_im2shift.shp'), self.shift.poly,   self.shift.prj)
            IO.write_shp(os.path.join(self.path_verbose_out, 'overlap_poly.shp'),  self.overlap_poly, self.ref.prj)

        ### FIXME: transform_mapPt1_to_mapPt2(im2shift_center_map, ds_imref.GetProjection(), ds_im2shift.GetProjection()) # später basteln für den fall, dass projektionen nicht gleich sind

        # get_clip_window_properties
        self._get_opt_winpos_winsize()
        if not self.q: print('Matching window position (X,Y): %s/%s' % (self.win_pos_XY[0], self.win_pos_XY[1]))
288
        self._get_clip_window_properties() # sets self.matchBox, self.otherBox and much more
289

290
        if self.v and self.path_verbose_out and self.matchBox.mapPoly and self.success is not False:
291
            IO.write_shp(os.path.join(self.path_verbose_out, 'poly_matchWin.shp'),
292
                         self.matchBox.mapPoly, self.matchBox.prj)
293

294
        self.success     = False if self.success is False or not self.matchBox.boxMapYX else None
295
        self._coreg_info = None # private attribute to be filled by self.coreg_info property
296
297
298


    def _set_outpathes(self, im_ref, im_tgt):
299
300
301
302
        assert isinstance(im_ref, (GeoArray, str)) and isinstance(im_tgt, (GeoArray, str)),\
            'COREG._set_outpathes() expects two file pathes (string) or two instances of the ' \
            'GeoArray class. Received %s and %s.' %(type(im_ref), type(im_tgt))

303
304
305
306
307
308
        get_baseN = lambda path: os.path.splitext(os.path.basename(path))[0]

        # get input pathes
        path_im_ref = im_ref.filePath if isinstance(im_ref, GeoArray) else im_ref
        path_im_tgt = im_tgt.filePath if isinstance(im_tgt, GeoArray) else im_tgt

309
310
311
312
313
        if self.path_out: # this also applies to self.path_out='auto'

            if self.path_out == 'auto':
                dir_out, fName_out = os.path.dirname(path_im_tgt), ''
            else:
314
                dir_out, fName_out = os.path.split(self.path_out)
315
316
317
318
319
320
321
322
323
324
325
326
327
328

            if dir_out and fName_out:
                # a valid output path is given => do nothing
                pass

            else:
                # automatically create an output directory and filename if not given
                if not dir_out:
                    if not path_im_ref:
                        dir_out = os.path.abspath(os.path.curdir)
                    else:
                        dir_out = os.path.dirname(path_im_ref)

                if not fName_out:
329
330
331
332
333
                    ext         = 'bsq' if self.fmt_out=='ENVI' else \
                                    gdal.GetDriverByName(self.fmt_out).GetMetadataItem(gdal.DMD_EXTENSION)
                    fName_out   = fName_out if not fName_out in ['.',''] else '%s__shifted_to__%s' \
                                    %(get_baseN(path_im_tgt), get_baseN(path_im_ref))
                    fName_out   = fName_out+'.%s'%ext if ext else fName_out
334

335
                self.path_out   = os.path.abspath(os.path.join(dir_out,fName_out))
336
337
338
339

                assert ' ' not in self.path_out, \
                    "The path of the output image contains whitespaces. This is not supported by GDAL."
        else:
340
            # this only happens if COREG is not instanced from within Python and self.path_out is explicitly set to None
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
            # => DESHIFTER will return an array
            pass

        if self.v:
            if self.path_verbose_out:
                dir_out, dirname_out = os.path.split(self.path_verbose_out)

                if not dir_out:
                    if self.path_out:
                        self.path_verbose_out = os.path.dirname(self.path_out)
                    else:
                        self.path_verbose_out = os.path.abspath(os.path.join(os.path.curdir,
                            'CoReg_verboseOut__%s__shifted_to__%s' % (get_baseN(path_im_tgt), get_baseN(path_im_ref))))
                elif dirname_out and not dir_out:
                    self.path_verbose_out = os.path.abspath(os.path.join(os.path.curdir, dirname_out))

                assert ' ' not in self.path_verbose_out, \
                    "'path_verbose_out' contains whitespaces. This is not supported by GDAL."

        else:
            self.path_verbose_out = None

        if self.path_verbose_out and not os.path.isdir(self.path_verbose_out): os.makedirs(self.path_verbose_out)


    def _get_image_params(self):
367
368
        self.ref   = GeoArray_CoReg(self.params,'ref')
        self.shift = GeoArray_CoReg(self.params,'shift')
369
        assert prj_equal(self.ref.prj, self.shift.prj), \
370
371
            'Input projections are not equal. Different projections are currently not supported. Got %s / %s.'\
            %(get_proj4info(proj=self.ref.prj), get_proj4info(proj=self.shift.prj))
372
373


374
375
376
377
378
    def equalize_pixGrids(self):
        """
        Equalize image grids and projections of reference and target image (align target to reference).
        """
        if not (prj_equal(self.ref.prj, self.shift.prj) and self.ref.xygrid_specs==self.shift.xygrid_specs):
379
            self.shift.arr = self.shift[:,:,self.shift.band4match]
380
381
382
            self.shift.reproject_to_new_grid(prototype=self.ref)


383
384
385
386
387
388
389
390
391
    def show_image_footprints(self):
        """This method is intended to be called from Jupyter Notebook and shows a web map containing the calculated
        footprints of the input images as well as the corresponding overlap area."""
        # TODO different colors for polygons
        assert self.overlap_poly, 'Please calculate the overlap polygon first.'

        try:
            import folium, geojson
        except ImportError:
392
393
            folium, geojson = None, None
        if not folium or not geojson:
394
395
396
            raise ImportError("This method requires the libraries 'folium' and 'geojson'. They can be installed with "
                              "the shell command 'pip install folium geojson'.")

397
398
399
400
        refPoly      = reproject_shapelyGeometry(self.ref  .poly      , self.ref  .epsg, 4326)
        shiftPoly    = reproject_shapelyGeometry(self.shift.poly      , self.shift.epsg, 4326)
        overlapPoly  = reproject_shapelyGeometry(self.overlap_poly    , self.shift.epsg, 4326)
        matchBoxPoly = reproject_shapelyGeometry(self.matchBox.mapPoly, self.shift.epsg, 4326)
401
402

        m = folium.Map(location=tuple(np.array(overlapPoly.centroid.coords.xy).flatten())[::-1])
403
        for poly in [refPoly, shiftPoly, overlapPoly, matchBoxPoly]:
404
405
406
407
408
            gjs = geojson.Feature(geometry=poly, properties={})
            folium.GeoJson(gjs).add_to(m)
        return m


409
410
    def show_matchWin(self, figsize=(15,15), interactive=True, deshifted=False):
        """Show the image content within the matching window.
411

412
413
414
415
416
        :param figsize:      <tuple> figure size
        :param interactive:  <bool> whether to return an interactive figure based on 'holoviews' library
        :param deshifted:    <bool> whether to put the image content AFTER shift correction into the figure
        :return:
        """
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
        if interactive:
            # use Holoviews
            try:
                import holoviews as hv
            except ImportError:
                hv =None
            if not hv:
                raise ImportError(
                    "This method requires the library 'holoviews'. It can be installed for Anaconda with "
                    "the shell command 'conda install -c ioam holoviews bokeh'.")
            warnings.filterwarnings('ignore')
            hv.notebook_extension('matplotlib')
            hv.Store.add_style_opts(hv.Image, ['vmin','vmax'])

            #hv.Store.option_setters.options().Image = hv.Options('style', cmap='gnuplot2')
            #hv.Store.add_style_opts(hv.Image, ['cmap'])
            #renderer = hv.Store.renderers['matplotlib'].instance(fig='svg', holomap='gif')
            #RasterPlot = renderer.plotting_class(hv.Image)
            #RasterPlot.cmap = 'gray'
436
437
            otherWin_corr       = self._get_deshifted_otherWin()
            xmin,xmax,ymin,ymax = self.matchBox.boundsMap
438
439
440
441


            get_vmin     = lambda arr: np.percentile(arr, 2)
            get_vmax     = lambda arr: np.percentile(arr, 98)
442
443
444
            rescale      = lambda arr: rescale_intensity(arr, in_range=(get_vmin(arr), get_vmax(arr)))
            get_arr      = lambda geoArr: rescale(np.ma.masked_equal(geoArr[:], geoArr.nodata))
            get_hv_image = lambda geoArr: hv.Image(get_arr(geoArr), bounds=(xmin,ymin,xmax,ymax))(
445
                style={'cmap':'gray',
446
                       'vmin':get_vmin(geoArr[:]), 'vmax':get_vmax(geoArr[:]), # does not work
447
                       'interpolation':'none'},
448
                plot={'fig_inches':figsize, 'show_grid':True})
449
450
                #plot={'fig_size':100, 'show_grid':True})

451
452
453
            imgs_orig = {1 : get_hv_image(self.matchWin), 2 : get_hv_image(self.otherWin)}
            imgs_corr = {1 : get_hv_image(self.matchWin), 2 : get_hv_image(otherWin_corr)}
            #layout = get_hv_image(self.matchWin) + get_hv_image(self.otherWin)
454

455
456
            imgs = {1 : get_hv_image(self.matchWin) + get_hv_image(self.matchWin),
                    2 : get_hv_image(self.otherWin) + get_hv_image(otherWin_corr)
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
                        }

            # Construct a HoloMap by evaluating the function over all the keys
            hmap_orig = hv.HoloMap(imgs_orig, kdims=['image'])
            hmap_corr = hv.HoloMap(imgs_corr, kdims=['image'])

            hmap      = hv.HoloMap(imgs, kdims=['image']).collate().cols(1) # displaying this results in a too small figure
            #hmap = hv.HoloMap(imgs_corr, kdims=['image']) +  hv.HoloMap(imgs_corr, kdims=['image'])

            ## Construct a HoloMap by defining the sampling on the Dimension
            #dmap = hv.DynamicMap(image_slice, kdims=[hv.Dimension('z_axis', values=keys)])
            warnings.filterwarnings('default')
            #return hmap

            return hmap_orig if not deshifted else hmap_corr

473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
        else:
            # TODO add titles
            self.matchWin.show(figsize=figsize)
            if deshifted:
                self._get_deshifted_otherWin().show(figsize=figsize)
            else:
                self.otherWin.show(figsize=figsize)


    def show_cross_power_spectrum(self, interactive=False):
        """
        Shows a 3D surface of the cross power spectrum resulting from phase correlating the reference and target
        image within the matching window.

        :param interactive:  whether to return an interactice 3D surface plot based on 'plotly' library
        :return:
        """

        if interactive:
            # create plotly 3D surface

            #import plotly.plotly as py # online mode -> every plot is uploaded into online plotly account
            from plotly.offline import iplot, init_notebook_mode
            import plotly.graph_objs as go

            init_notebook_mode(connected=True)

            z_data = self._calc_shifted_cross_power_spectrum()
            data   = [go.Surface(z=z_data)]
            layout = go.Layout(
                title='cross power spectrum',
                autosize=False,
                width=1000,
                height=1000,
                margin=dict(l=65, r=50, b=65, t=90))
            fig    = go.Figure(data=data, layout=layout)

            return iplot(fig, filename='SCPS')

        else:
            # use matplotlib
            scps = self._calc_shifted_cross_power_spectrum()
            PLT.subplot_3dsurface(scps.astype(np.float32))

517

518
    def _get_opt_winpos_winsize(self):
519
        # type: (tuple,tuple) -> None
520
521
522
523
        """
        Calculates optimal window position and size in reference image units according to DGM, cloud_mask and
        trueCornerLonLat.
        """
524
525
526
527
528
529
530
531
532
533
534
535
        # dummy algorithm: get center position of overlap instead of searching ideal window position in whole overlap
        # TODO automatischer Algorithmus zur Bestimmung der optimalen Window Position

        wp = tuple(self.win_pos_XY)
        assert type(self.win_pos_XY) in [tuple,list,np.ndarray],\
            'The window position must be a tuple of two elements. Got %s with %s elements.' %(type(wp),len(wp))
        wp = tuple(wp)

        if None in wp:
            overlap_center_pos_x, overlap_center_pos_y = self.overlap_poly.centroid.coords.xy
            wp = (wp[0] if wp[0] else overlap_center_pos_x[0]), (wp[1] if wp[1] else overlap_center_pos_y[0])

536
        # validate window position
537
538
539
540
541
542
        if not self.overlap_poly.contains(Point(wp)):
            self.success=False
            self.tracked_errors.append(ValueError('The provided window position %s/%s is outside of the overlap ' \
                                                  'area of the two input images. Check the coordinates.' %wp))
            if not self.ignErr:
                raise self.tracked_errors[-1]
543
544
545
546
547
548

        # check if window position is within bad data area if a respective mask has been provided
        for im in [self.ref, self.shift]:
            if im.mask_baddata is not None:
                imX, imY = mapXY2imXY(wp, im.mask_baddata.gt)

549
                if im.mask_baddata[int(imY), int(imX)] is True:
550
551
552
553
554
555
556
                    self.tracked_errors.append(
                        RuntimeError('According to the provided bad data mask for the %s the chosen window position '
                            '%s / %s is within a bad data area. Using this window position for coregistration '
                            'is not reasonable. Please provide a better window position!' %(im.imName, wp[0], wp[1])))
                    self.success = False
                    if not self.ignErr:
                        raise self.tracked_errors[-1]
557

558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
        self.win_pos_XY  = wp
        self.win_size_XY = (int(self.win_size_XY[0]), int(self.win_size_XY[1])) if self.win_size_XY else (512,512)


    def _get_clip_window_properties(self):
        """Calculate all properties of the matching window and the other window. These windows are used to read the
        corresponding image positions in the reference and the target image.
        hint: Even if X- and Y-dimension of the target window is equal, the output window can be NOT quadratic!
        """
        # FIXME image sizes like 10000*256 are still possible

        wpX,wpY             = self.win_pos_XY
        wsX,wsY             = self.win_size_XY
        ref_wsX, ref_wsY    = (wsX*self.ref.xgsd  , wsY*self.ref.ygsd)   # image units -> map units
        shift_wsX,shift_wsY = (wsX*self.shift.xgsd, wsY*self.shift.ygsd) # image units -> map units
        ref_box_kwargs      = {'wp':(wpX,wpY),'ws':(ref_wsX,ref_wsY)    ,'gt':self.ref.gt  }
        shift_box_kwargs    = {'wp':(wpX,wpY),'ws':(shift_wsX,shift_wsY),'gt':self.shift.gt}
575
576
        matchBox            = boxObj(**ref_box_kwargs)   if self.grid2use=='ref' else boxObj(**shift_box_kwargs)
        otherBox            = boxObj(**shift_box_kwargs) if self.grid2use=='ref' else boxObj(**ref_box_kwargs)
577
578
579
        overlapWin          = boxObj(mapPoly=self.overlap_poly,gt=self.ref.gt)

        # clip matching window to overlap area
580
581
582
583
584
585
586
587
588
589
590
591
592
        matchBox.mapPoly = matchBox.mapPoly.intersection(overlapWin.mapPoly)

        #check if matchBox extent touches no data area of the image -> if yes: shrink it
        overlapPoly_within_matchWin = matchBox.mapPoly.intersection(self.overlap_poly)
        if overlapPoly_within_matchWin.area < matchBox.mapPoly.area:
            wsX_start, wsY_start = 1 if wsX>=wsY else wsX/wsY, 1 if wsY>=wsX else wsY/wsX
            box = boxObj(**dict(wp=(wpX,wpY),ws=(wsX_start, wsY_start), gt=matchBox.gt))
            while True:
                box.buffer_imXY(1,1)
                if not box.mapPoly.within(overlapPoly_within_matchWin):
                    box.buffer_imXY(-1, -1)
                    matchBox = box
                    break
593
594
595

        # move matching window to imref grid or im2shift grid
        mW_rows, mW_cols = (self.ref.rows, self.ref.cols) if self.grid2use == 'ref' else (self.shift.rows, self.shift.cols)
596
        matchBox.mapPoly = move_shapelyPoly_to_image_grid(matchBox.mapPoly, matchBox.gt, mW_rows, mW_cols, 'NW')
597

598
599
        # check, ob durch Verschiebung auf Grid die matchBox außerhalb von overlap_poly geschoben wurde
        if not matchBox.mapPoly.within(overlapWin.mapPoly):
600
            # matchPoly weiter verkleinern # 1 px buffer reicht, weil window nur auf das Grid verschoben wurde
601
602
            xLarger,yLarger = matchBox.is_larger_DimXY(overlapWin.boundsIm)
            matchBox.buffer_imXY(-1 if xLarger else 0, -1 if yLarger else 0)
603
604

        # matching_win direkt auf grid2use (Rundungsfehler bei Koordinatentrafo beseitigen)
605
        matchBox.imPoly = round_shapelyPoly_coords(matchBox.imPoly, precision=0, out_dtype=int)
606
607

        # Check, ob match Fenster größer als anderes Fenster
608
        if not (matchBox.mapPoly.within(otherBox.mapPoly) or matchBox.mapPoly==otherBox.mapPoly):
609
            # dann für anderes Fenster kleinstes Fenster finden, das match-Fenster umgibt
610
            otherBox.boxImYX = get_smallest_boxImYX_that_contains_boxMapYX(matchBox.boxMapYX,otherBox.gt)
611
612

        # evtl. kann es sein, dass bei Shift-Fenster-Vergrößerung das shift-Fenster zu groß für den overlap wird
613
        while not otherBox.mapPoly.within(overlapWin.mapPoly):
614
            # -> match Fenster verkleinern und neues anderes Fenster berechnen
615
616
617
618
            xLarger, yLarger = otherBox.is_larger_DimXY(overlapWin.boundsIm)
            matchBox.buffer_imXY(-1 if xLarger else 0, -1 if yLarger else 0)
            previous_area    = otherBox.mapPoly.area
            otherBox.boxImYX = get_smallest_boxImYX_that_contains_boxMapYX(matchBox.boxMapYX,otherBox.gt)
619

620
            if previous_area == otherBox.mapPoly.area:
621
622
623
624
625
626
627
628
629
630
631
632
                self.tracked_errors.append(
                    RuntimeError('Matching window in target image is larger than overlap area but further shrinking '
                                 'the matching window is not possible. Check if the footprints of the input data have '
                                 'been computed correctly. '))
                if not self.ignErr:
                    raise self.tracked_errors[-1]
                break # break out of while loop in order to avoid that code gets stuck here

        if self.tracked_errors:
            self.success = False
        else:
            # check results
633
634
            assert matchBox.mapPoly.within(otherBox.mapPoly)
            assert otherBox.mapPoly.within(overlapWin.mapPoly)
635
636

            self.imfft_gsd              = self.ref.xgsd       if self.grid2use =='ref' else self.shift.xgsd
637
638
            self.ref.win,self.shift.win = (matchBox,otherBox) if self.grid2use =='ref' else (otherBox,matchBox)
            self.matchBox,self.otherBox = matchBox, otherBox
639
640
            self.ref.  win.size_YX      = tuple([int(i) for i in self.ref.  win.imDimsYX])
            self.shift.win.size_YX      = tuple([int(i) for i in self.shift.win.imDimsYX])
641
            match_win_size_XY           = tuple(reversed([int(i) for i in matchBox.imDimsYX]))
642
643
644
            if not self.q and match_win_size_XY != self.win_size_XY:
                print('Target window size %s not possible due to too small overlap area or window position too close '
                      'to an image edge. New matching window size: %s.' %(self.win_size_XY,match_win_size_XY))
645
646
            #IO.write_shp('/misc/hy5/scheffler/Temp/matchMapPoly.shp', matchBox.mapPoly,matchBox.prj)
            #IO.write_shp('/misc/hy5/scheffler/Temp/otherMapPoly.shp', otherBox.mapPoly,otherBox.prj)
647
648
649
650
651
652
653


    def _get_image_windows_to_match(self):
        """Reads the matching window and the other window using subset read, and resamples the other window to the
        resolution and the pixel grid of the matching window. The result consists of two images with the same
        dimensions and exactly the same corner coordinates."""

654
655
656
657
658
659
660
661
        match_fullGeoArr = self.ref   if self.grid2use=='ref' else self.shift
        other_fullGeoArr = self.shift if self.grid2use=='ref' else self.ref
        self.matchWin = GeoArray(np.array([]), copy(match_fullGeoArr.gt), copy(match_fullGeoArr.prj),
                                 nodata=copy(match_fullGeoArr.nodata)) # array data is overwritten later
        self.otherWin = GeoArray(np.array([]), copy(other_fullGeoArr.gt), copy(other_fullGeoArr.prj),
                                 nodata=copy(other_fullGeoArr.nodata)) # array data is overwritten later
        self.matchWin.imID = match_fullGeoArr.imID
        self.otherWin.imID = other_fullGeoArr.imID
662
663

        # matchWin per subset-read einlesen -> self.matchWin.data
664
        rS, rE, cS, cE = GEO.get_GeoArrayPosition_from_boxImYX(self.matchBox.boxImYX)
665
        assert np.array_equal(np.abs(np.array([rS,rE,cS,cE])), np.array([rS,rE,cS,cE])), \
666
667
668
            'Got negative values in gdalReadInputs for %s.' %match_fullGeoArr.imName
        self.matchWin.arr = match_fullGeoArr[rS:rE,cS:cE, match_fullGeoArr.band4match]
        self.matchWin.gt  = GEO.get_subset_GeoTransform(match_fullGeoArr.gt, self.matchBox.boxImYX)
669
670

        # otherWin per subset-read einlesen
671
        rS, rE, cS, cE = GEO.get_GeoArrayPosition_from_boxImYX(self.otherBox.boxImYX)
672
        assert np.array_equal(np.abs(np.array([rS,rE,cS,cE])), np.array([rS,rE,cS,cE])), \
673
674
675
676
677
678
            'Got negative values in gdalReadInputs for %s.' %other_fullGeoArr.imName
        self.otherWin.arr = other_fullGeoArr[rS:rE, cS:cE, other_fullGeoArr.band4match]
        self.otherWin.gt  = GEO.get_subset_GeoTransform(other_fullGeoArr.gt, self.otherBox.boxImYX)

        #self.matchWin.deepcopy_array()
        #self.otherWin.deepcopy_array()
679
680
681

        if self.v:
            print('Original matching windows:')
682
683
            ref_data, shift_data =  (self.matchWin[:], self.otherWin[:]) if self.grid2use=='ref' else \
                                    (self.otherWin[:], self.matchWin[:])
684
685
            PLT.subplot_imshow([ref_data, shift_data],[self.ref.title,self.shift.title], grid=True)

686
        # resample otherWin.arr to the resolution of matchWin AND make sure the pixel edges are identical
687
688
        # (in order to make each image show the same window with the same coordinates)
        # TODO replace cubic resampling by PSF resampling - average resampling leads to sinus like distortions in the fft image that make a precise coregistration impossible. Thats why there is currently no way around cubic resampling.
689
        tgt_xmin,tgt_xmax,tgt_ymin,tgt_ymax = self.matchBox.boundsMap
690
691
692
693
694
695
696
697
698
699
700
701
702
703

        # equalize pixel grids and projection of matchWin and otherWin (ONLY if grids are really different)
        if not(self.matchWin.xygrid_specs==self.otherWin.xygrid_specs and
            prj_equal(self.matchWin.prj, self.otherWin.prj)):
            self.otherWin.arr, self.otherWin.gt = warp_ndarray(self.otherWin.arr,
                                                               self.otherWin.gt,
                                                               self.otherWin.prj,
                                                               self.matchWin.prj,
                                                               out_gsd    = (self.imfft_gsd, self.imfft_gsd),
                                                               out_bounds = ([tgt_xmin, tgt_ymin, tgt_xmax, tgt_ymax]),
                                                               rspAlg     = _dict_rspAlg_rsp_Int[self.rspAlg_calc],
                                                               in_nodata  = self.otherWin.nodata,
                                                               CPUs       = None if self.mp else 1,
                                                               progress   = False) [:2]
704
705

        if self.matchWin.shape != self.otherWin.shape:
706
707
            self.tracked_errors.append(
                RuntimeError('Bad output of get_image_windows_to_match. Reference image shape is %s whereas shift '
708
                             'image shape is %s.' % (self.matchWin.shape, self.otherWin.shape)))
709
            raise self.tracked_errors[-1]
710
711
        rows, cols = [i if i % 2 == 0 else i - 1 for i in self.matchWin.shape]
        self.matchWin.arr, self.otherWin.arr = self.matchWin.arr[:rows, :cols], self.otherWin.arr[:rows, :cols]
712

713
        assert self.matchWin.arr is not None and self.otherWin.arr is not None, 'Creation of matching windows failed.'
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740


    @staticmethod
    def _shrink_winsize_to_binarySize(win_shape_YX, target_size=None):
        # type: (tuple, tuple, int , int) -> tuple
        """Shrinks a given window size to the closest binary window size (a power of 2) -
        separately for X- and Y-dimension.

        :param win_shape_YX:    <tuple> source window shape as pixel units (rows,colums)
        :param target_size:     <tuple> source window shape as pixel units (rows,colums)
        """

        binarySizes   = [2**i for i in range(3,14)] # [8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]
        possibSizes_X = [i for i in binarySizes if i <= win_shape_YX[1]]
        possibSizes_Y = [i for i in binarySizes if i <= win_shape_YX[0]]
        if possibSizes_X and possibSizes_Y:
            tgt_size_X,tgt_size_Y = target_size if target_size else (max(possibSizes_X),max(possibSizes_Y))
            closest_to_target_X = int(min(possibSizes_X, key=lambda x:abs(x-tgt_size_X)))
            closest_to_target_Y = int(min(possibSizes_Y, key=lambda y:abs(y-tgt_size_Y)))
            return closest_to_target_Y,closest_to_target_X
        else:
            return None


    def _calc_shifted_cross_power_spectrum(self, im0=None, im1=None, precision=np.complex64):
        """Calculates shifted cross power spectrum for quantifying x/y-shifts.

741
742
743
744
        :param im0:         reference image
        :param im1:         subject image to shift
        :param precision:   to be quantified as a datatype
        :return:            2D-numpy-array of the shifted cross power spectrum
745
746
        """

747
748
749
        im0 = im0 if im0 is not None else self.matchWin[:] if self.matchWin.imID=='ref'   else self.otherWin[:]
        im1 = im1 if im1 is not None else self.otherWin[:] if self.otherWin.imID=='shift' else self.matchWin[:]

750
751
752
753
754
        assert im0.shape == im1.shape, 'The reference and the target image must have the same dimensions.'
        if im0.shape[0]%2!=0: warnings.warn('Odd row count in one of the match images!')
        if im1.shape[1]%2!=0: warnings.warn('Odd column count in one of the match images!')

        wsYX = self._shrink_winsize_to_binarySize(im0.shape) if self.bin_ws              else im0.shape
755
        wsYX = ((min(wsYX),) * 2                             if self.force_quadratic_win else wsYX) if wsYX else None
756
757
758
759
760
761
762

        if wsYX:
            time0 = time.time()
            if self.v: print('final window size: %s/%s (X/Y)' % (wsYX[1], wsYX[0]))
            center_YX = np.array(im0.shape)/2
            xmin,xmax,ymin,ymax = int(center_YX[1]-wsYX[1]/2), int(center_YX[1]+wsYX[1]/2),\
                                  int(center_YX[0]-wsYX[0]/2), int(center_YX[0]+wsYX[0]/2)
763

764
765
766
767
768
769
770
            in_arr0  = im0[ymin:ymax,xmin:xmax].astype(precision)
            in_arr1  = im1[ymin:ymax,xmin:xmax].astype(precision)

            if self.v:
                PLT.subplot_imshow([in_arr0.astype(np.float32), in_arr1.astype(np.float32)],
                               ['FFTin '+self.ref.title,'FFTin '+self.shift.title], grid=True)

771
            if pyfftw and self.fftw_works is not False: # if module is installed and working
772
773
                fft_arr0 = pyfftw.FFTW(in_arr0,np.empty_like(in_arr0), axes=(0,1))()
                fft_arr1 = pyfftw.FFTW(in_arr1,np.empty_like(in_arr1), axes=(0,1))()
774
775
776
777
778
779
780
781
782

                # catch empty output arrays (for some reason this happens sometimes..) -> use numpy fft
                if self.fftw_works is None and (np.std(fft_arr0)==0 or np.std(fft_arr1)==0):
                    self.fftw_works = False
                    # recreate input arrays and use numpy fft as fallback
                    in_arr0 = im0[ymin:ymax, xmin:xmax].astype(precision)
                    in_arr1 = im1[ymin:ymax, xmin:xmax].astype(precision)
                    fft_arr0 = np.fft.fft2(in_arr0)
                    fft_arr1 = np.fft.fft2(in_arr1)
783
784
                else:
                    self.fftw_works = True
785
786
787
            else:
                fft_arr0 = np.fft.fft2(in_arr0)
                fft_arr1 = np.fft.fft2(in_arr1)
788

789
790
791
            #GeoArray(fft_arr0.astype(np.float32)).show(figsize=(15,15))
            #GeoArray(fft_arr1.astype(np.float32)).show(figsize=(15,15))

792
793
794
795
796
            if self.v: print('forward FFTW: %.2fs' %(time.time() -time0))

            eps = np.abs(fft_arr1).max() * 1e-15
            # cps == cross-power spectrum of im0 and im2

797
            temp = np.array(fft_arr0 * fft_arr1.conjugate()) / (np.abs(fft_arr0) * np.abs(fft_arr1) + eps)
798
799
800

            time0 = time.time()
            if 'pyfft' in globals():
801
                ifft_arr = pyfftw.FFTW(temp,np.empty_like(temp), axes=(0,1), direction='FFTW_BACKWARD')()
802
803
804
805
806
807
808
809
810
811
812
813
814
            else:
                ifft_arr = np.fft.ifft2(temp)
            if self.v: print('backward FFTW: %.2fs' %(time.time() -time0))

            cps = np.abs(ifft_arr)
            # scps = shifted cps
            scps = np.fft.fftshift(cps)
            if self.v:
                PLT.subplot_imshow([in_arr0.astype(np.uint16), in_arr1.astype(np.uint16), fft_arr0.astype(np.uint8),
                                fft_arr1.astype(np.uint8), scps], titles=['matching window im0', 'matching window im1',
                                "fft result im0", "fft result im1", "cross power spectrum"], grid=True)
                PLT.subplot_3dsurface(scps.astype(np.float32))
        else:
815
            self.success = False
816
817
818
819
820
821
822
823
824
825
826
827
828
            self.tracked_errors.append(
                RuntimeError('The matching window became too small for calculating a reliable match. Matching failed.'))
            if self.ignErr:
                scps = None
            else:
                raise self.tracked_errors[-1]

        self.fftw_win_size_YX = wsYX
        return scps


    @staticmethod
    def _get_peakpos(scps):
829
830
831
832
833
        """Returns the row/column position of the peak within the given cross power spectrum.

        :param scps: <np.ndarray> shifted cross power spectrum
        :return:     <np.ndarray> [row, column>
        """
834
        max_flat_idx = np.argmax(scps)
835
        return np.array(np.unravel_index(max_flat_idx, scps.shape))
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877


    @staticmethod
    def _get_shifts_from_peakpos(peakpos, arr_shape):
        y_shift = peakpos[0]-arr_shape[0]//2
        x_shift = peakpos[1]-arr_shape[1]//2
        return x_shift,y_shift


    @staticmethod
    def _clip_image(im, center_YX, winSzYX): # TODO this is also implemented in GeoArray
        get_bounds = lambda YX,wsY,wsX: (int(YX[1]-(wsX/2)),int(YX[1]+(wsX/2)),int(YX[0]-(wsY/2)),int(YX[0]+(wsY/2)))
        wsY,wsX    = winSzYX
        xmin,xmax,ymin,ymax = get_bounds(center_YX,wsY,wsX)
        return im[ymin:ymax,xmin:xmax]


    def _get_grossly_deshifted_images(self, im0, im1, x_intshift, y_intshift): # TODO this is also implemented in GeoArray # this should update ref.win.data and shift.win.data
        # get_grossly_deshifted_im0
        old_center_YX = np.array(im0.shape)/2
        new_center_YX = [old_center_YX[0]+y_intshift, old_center_YX[1]+x_intshift]

        x_left  = new_center_YX[1]
        x_right = im0.shape[1]-new_center_YX[1]
        y_above = new_center_YX[0]
        y_below = im0.shape[0]-new_center_YX[0]
        maxposs_winsz = 2*min(x_left,x_right,y_above,y_below)

        gdsh_im0 = self._clip_image(im0, new_center_YX, [maxposs_winsz, maxposs_winsz])

        # get_corresponding_im1_clip
        crsp_im1  = self._clip_image(im1, np.array(im1.shape) / 2, gdsh_im0.shape)

        if self.v:
            PLT.subplot_imshow([self._clip_image(im0, old_center_YX, gdsh_im0.shape), crsp_im1],
                               titles=['reference original', 'target'], grid=True)
            PLT.subplot_imshow([gdsh_im0, crsp_im1], titles=['reference virtually shifted', 'target'], grid=True)
        return gdsh_im0,crsp_im1


    @staticmethod
    def _find_side_maximum(scps, v=0):
878
        centerpos     = [scps.shape[0]//2, scps.shape[1]//2]
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
        profile_left  = scps[ centerpos [0]  ,:centerpos[1]+1]
        profile_right = scps[ centerpos [0]  , centerpos[1]:]
        profile_above = scps[:centerpos [0]+1, centerpos[1]]
        profile_below = scps[ centerpos [0]: , centerpos[1]]

        if v:
            max_count_vals = 10
            PLT.subplot_2dline([[range(len(profile_left)) [-max_count_vals:], profile_left[-max_count_vals:]],
                                [range(len(profile_right))[:max_count_vals] , profile_right[:max_count_vals]],
                                [range(len(profile_above))[-max_count_vals:], profile_above[-max_count_vals:]],
                                [range(len(profile_below))[:max_count_vals:], profile_below[:max_count_vals]]],
                                titles =['Profile left', 'Profile right', 'Profile above', 'Profile below'],
                                shapetuple=(2,2))

        get_sidemaxVal_from_profile = lambda pf: np.array(pf)[::-1][1] if pf[0]<pf[-1] else np.array(pf)[1]
        sm_dicts_lr  = [{'side':si, 'value': get_sidemaxVal_from_profile(pf)} \
                        for pf,si in zip([profile_left,profile_right],['left','right'])]
        sm_dicts_ab  = [{'side':si, 'value': get_sidemaxVal_from_profile(pf)} \
                        for pf,si in zip([profile_above,profile_below],['above','below'])]
        sm_maxVal_lr = max([i['value'] for i in sm_dicts_lr])
        sm_maxVal_ab = max([i['value'] for i in sm_dicts_ab])
        sidemax_lr   = [sm for sm in sm_dicts_lr if sm['value'] is sm_maxVal_lr][0]
        sidemax_ab   = [sm for sm in sm_dicts_ab if sm['value'] is sm_maxVal_ab][0]
        sidemax_lr['direction_factor'] = {'left':-1, 'right':1} [sidemax_lr['side']]
        sidemax_ab['direction_factor'] = {'above':-1,'below':1} [sidemax_ab['side']]

        if v:
            print('Horizontal side maximum found %s. value: %s' %(sidemax_lr['side'],sidemax_lr['value']))
907
            print('Vertical side maximum found %s. value: %s'   %(sidemax_ab['side'],sidemax_ab['value']))
908
909
910
911
912
913
914
915
916
917

        return sidemax_lr, sidemax_ab


    def _calc_integer_shifts(self, scps):
        peakpos = self._get_peakpos(scps)
        x_intshift, y_intshift = self._get_shifts_from_peakpos(peakpos, scps.shape)
        return x_intshift, y_intshift


918
    def _calc_shift_reliability(self, scps):
919
920
921
922
923
924
925
926
927
928
929
930
931
932
        """Calculates a confidence percentage that can be used as an assessment for reliability of the calculated shifts.

        :param scps:    <np.ndarray> shifted cross power spectrum
        :return:
        """

        # calculate mean power at peak
        peakR, peakC  = self._get_peakpos(scps)
        power_at_peak = np.mean(scps[peakR-1:peakR+2, peakC-1:peakC+2])

        # calculate mean power without peak + 3* standard deviation
        scps_masked        = scps
        scps_masked[peakR-1:peakR+2, peakC-1:peakC+2] = -9999
        scps_masked        = np.ma.masked_equal(scps_masked, -9999)
933
        power_without_peak = np.mean(scps_masked) + 2* np.std(scps_masked)
934
935
936
937
938
939

        # calculate confidence
        confid = 100-((power_without_peak/power_at_peak)*100)
        confid = 100 if confid > 100 else 0 if confid < 0 else confid

        if not self.q:
940
            print('Estimated reliability of the calculated shifts:  %.1f' %confid, '%')
941
942
943
944

        return confid


945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
    def _validate_integer_shifts(self, im0, im1, x_intshift, y_intshift):

        if (x_intshift, y_intshift)!=(0,0):
            # temporalily deshift images on the basis of calculated integer shifts
            gdsh_im0, crsp_im1 = self._get_grossly_deshifted_images(im0, im1, x_intshift, y_intshift)

            # check if integer shifts are now gone (0/0)
            scps = self._calc_shifted_cross_power_spectrum(gdsh_im0, crsp_im1)
            if scps is not None:
                peakpos = self._get_peakpos(scps)
                x_shift, y_shift = self._get_shifts_from_peakpos(peakpos, scps.shape)
                if (x_shift, y_shift) == (0,0):
                    return 'valid', 0, 0, scps
                else:
                    return 'invalid', x_shift, y_shift, scps
            else:
                return 'invalid', None, None, scps
        else:
            return 'valid', 0, 0, None


966
    def _calc_subpixel_shifts(self, scps):
967
968
969
970
971
972
973
974
975
976
977
        sidemax_lr, sidemax_ab = self._find_side_maximum(scps, self.v)
        x_subshift = (sidemax_lr['direction_factor']*sidemax_lr['value'])/(np.max(scps)+sidemax_lr['value'])
        y_subshift = (sidemax_ab['direction_factor']*sidemax_ab['value'])/(np.max(scps)+sidemax_ab['value'])
        return x_subshift, y_subshift


    @staticmethod
    def _get_total_shifts(x_intshift, y_intshift, x_subshift, y_subshift):
        return x_intshift+x_subshift, y_intshift+y_subshift


978
979
980
981
982
983
984
985
986
987
988
989
990
991
    def _get_deshifted_otherWin(self):
        """Returns a de-shifted version of self.otherWin as a GeoArray instance.The output dimensions and geographic
        bounds are equal to those of self.matchWin and geometric shifts are corrected according to the previously
        computed X/Y shifts within the matching window. This allows direct application of algorithms e.g. measuring
        image similarity.

        The image subset that is resampled in this function is always the same that has been resampled during
        computation of geometric shifts (usually the image with the higher geometric resolution).

        :returns:   GeoArray instance of de-shifted self.otherWin
        """

        # shift vectors have been calculated to fit target image onto reference image
        # -> so the shift vectors have to be inverted if shifts are applied to reference image
992
993
        coreg_info = self._get_inverted_coreg_info() if self.otherWin.imID=='ref' else self.coreg_info

994
995
        matchFull  = self.ref if self.matchWin.imID=='ref' else self.shift
        otherFull  = self.ref if self.otherWin.imID=='ref' else self.shift
996
997
998
999
        ds_results = DESHIFTER(otherFull, coreg_info,
                               band2process  = otherFull.band4match+1,
                               clipextent    = list(np.array(self.matchBox.boundsMap)[[0,2,1,3]]),
                               target_xyGrid = matchFull.xygrid_specs,
1000
                               q             = True